The counterfactual regret minimization (CFR) algorithm [1]}, [2]}, which converges to Nash equilibrium by constantly iterating to reduce regrets, has been proved to be successful in two-player zero-sum games with incomplete information through experiments. The core idea of CFR is to apply the regret minimization algorithms [3]}, [4]} to each infoset in order to calculate strategies. In other words, it divides the total regret into a number of regrets on infosets. The purpose of optimizing policy and finding NE is achieved by minimizing the cumulative regret. Let \(v^{\theta }_{i}(I)\)  be the counterfactual value of player \(i\in P(I)\)  at infoset \(I\) :
\(v^{\sigma }(I)=\sum _{z \in Z_{I}} \pi _{-p}^{\sigma }(z[I]) \pi ^{\sigma }(z[I] \rightarrow z) u_{p}(z)\) 
